Maritime Knowledge Base Semantic Application Semantic Exchange Workshop February 17th, 2009 Eric Freese Semantic Web, XML & Geospatial Technologist Copyright 2009 Northrop Grumman Corporation

2 Northrop Grumman Semantic Research Geospatial Semantic Web –Distributed Geospatial Data Sources Developed framework for geospatial information integration using OWL, SWRL and backwards rule processing with data sources using OGC standards –Conceptual Query - developed query templates to allow users to easily create complex queries –Geospatial Ontology Trade Study - investigated, compared and contrasted ontologies in geospatial domains –Developed Snoggle, a graphical tool to assist in ontology alignment and generate SWRL to transform RDF into target ontology Independent Research and Development (IRAD) –Advanced Geospatial Intelligence Application of Knowledge & Presentation of a Maritime Domain Awareness capability using Variable Rules Semantic Query Semantic Data Acquisition - Features and Unstructured Text Extraction –Advanced Search and Discovery Enables the integration, discovery, query, and ontology-based search of multiple, heterogeneous data sources and web services using semantic technologies –Geospatial Data Fusion Apply semantic technology to the fusion of multiple geospatial feature data sources (conflation) into a merged product –Scalable Holistic Discovery and Search Semantically, spatially, and temporally enhance Enterprise Search Copyright 2009 Northrop Grumman Corporation

3 Maritime Knowledge Base Maritime Knowledge Base Client Documents Uses ontology- based interface to search multiple maritime data holdings WWW Digitalseas.com – vessel data USCG – vessel data MarineTraffic.com – vessel, port and location (via AIS) data Worldwide Threat to Shipping – incident data International Maritime Bureau – piracy reports CIA World Factbook – country and port data E-ships.net – port data WorldPortSource.com – port data DBPedia – semantic Wikipedia Allegrograph Triple Store Copyright 2009 Northrop Grumman Corporation

4 Maritime Knowledge Base Data Discovery & Collection –Screen scraping – Java program developed to read HTML pages from websites, extract useful data using XQuery, and output RDF/OWL –Data conversion – databases or spreadsheets converted to XML and then converted to RDF/OWL using XSLT Custom Ontologies –Data driven ontologies were developed based on analysis of available data –Where possible, ontology items were reused from standard ontologies –owl:sameAs used to connect identical resources from different sources Connecting the Dots –SPARQL queries written to identify common items from datasets to connect them e.g., a ship with same name and same identifier (IMO, MMSI, etc.) probably the same ship. originally used inverse functional properties to do this automatically but dirty data made this unworkable Copyright 2009 Northrop Grumman Corporation

5 Maritime Knowledge Base Types of Data –Vessels – name, type, registration, capacity, propulsion, ownership –Positions – lat/long, time, destination, ETA –Ports – lat/long & country locations, sub-ports, aliases –Incidents – ships involved, dates/times, description, general area –Geographic – countries and other cultural data Data Volume – Over 4.9 million RDF triples –~ 80,000 ships –~ 118,000 locations –~ 5,000 ports in 245 countries – 964 incidents from 2006 to Present Copyright 2009 Northrop Grumman Corporation

6 Cross-Ontology Querying (SPARQL) Ontologies contain connection points for simpler references –Vessels reference location points; incidents reference ships involved; etc. Query examples: –List all data about a given ship (including positions and incidents) –List all the ports in a given country –List all ships that departed from a given port/country during a specific period –List incidents in a specified period by country –List all ships departing from ports in predominantly Muslim countries that are bound for the United States RDF/OWL data modeled to be self-describing –Possible to query a dataset without knowing anything about the data set at the outset Copyright 2009 Northrop Grumman Corporation

7 Conclusion Standardized ontologies will allow easier migration to Semantic Web –e.g., USCG or Lloyds could define a standard ontology for maritime data Standardized ontologies will facilitate interchange and processing of data between stakeholders Currently existing structured and unstructured data can be made ready for the Semantic Web fairly easily –Database schemata can provide an initial stepping off point –Tools exist that allow relational databases to be treated as Semantic Web repositories, including querying, inferencing, reasoning, etc. Copyright 2009 Northrop Grumman Corporation

8 Contact Information Eric Freese –(314) Jim Ressler –(314) Mailing address: –1010 Market St. Suite 1740, St. Louis, MO Copyright 2009 Northrop Grumman Corporation

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18 Copyright 2009 Northrop Grumman Corporation